Industrial machines may vary in their complexity, but typically include several rotating elements, such as bearings, shafts and gears. Where there is rotation there generally is some degree of machine vibration. Vibration occurs due to imbalance, misalignment of shafts, and bearing wear. Every machine has some level of vibration that is characteristic of its operation. In particular, properly maintained machines are designed to exhibit not more than a reasonable degree of vibration under normal operating conditions. Excessive levels of vibration indicate machine problems that may lead to unsatisfactory operation and eventual breakdown.
Machine malfunction can sometimes be detected by a change in the vibration pattern of a machine. In today's plants where machines are complex and/or large in number, engineers often rely on advanced methods of identifying abnormal levels and patterns of vibration in order to determine the condition of a machine. For example, based upon knowledge of the rotational speed of individual machine elements, machine maintenance personnel can monitor the machine's vibration level at certain characteristic frequencies to acquire an indication of the overall condition of the machine. As the mechanical integrity of a machine element begins to degrade, the vibration level associated with that element changes from its normal characteristic level. To the trained machine maintenance personnel such change may indicate that corrective action will soon be necessary. By implementing a machine monitoring program, the machine's vibration levels can be measured on a regular schedule, and early detection of abnormal machine operation is possible. With such early warning, repair of the machine may be scheduled well before a machine breakdown and the associated work stoppage occurs. In this manner, machine “down-time” may be scheduled well in advance so as to minimize the impact on manufacturing operations.
One challenge in predicting potential failures is determining what vibration characteristics may be considered normal and safe—i.e., what is the machine's normal characteristic level of vibration. Further, what are the variations in vibrations that fall outside the normal and may indicate machine underperformance, component wear, and ultimate machine breakdown. Establishing what is considered “normal” operating vibration signatures for machinery has been historically accomplished by selecting candidate machinery vibration tests from identical machines without mechanical faults, normalizing for speed variations, and numerically averaging them together to obtain an “average” vibration signature. Vibration spectra typically are collected at a number of locations on a given machine. Specific spectral features in the measured data may include harmonic families or difference families, which are associated with certain types of machinery faults.
When a new, identical machine is installed, it may be monitored by comparing its vibration profile with the historical average vibration profile previously obtained for that machine. This methodology is feasible and useful when a large number of historical sets of vibration tests for groups of machines are available. However, it is not feasible when there is a lack of such abundant test data from a variety of machines in various conditions. Furthermore, building average baselines requires sufficient knowledge and experience in machinery vibration signature analysis. In particular, the historical averaging operation is subjective. A user needs to determine whether an acquired candidate set of data is ‘averagable’—meaning whether it is good quality data from an apparently fault free machine. Many machine users do not have the skills or experience to judge whether test data is suitable for establishing a baseline.
Accordingly, there is need of an alternative method of obtaining a baseline vibration profile for machines, such as when data for similar machines without faults is not available. Further, there is a need to obtain vibration profiles that effectively define normal and that may be efficiently applied to desired machinery to screen out machines that are faulty or in need of maintenance, and to troubleshoot machines identified as being faulty or in need of maintenance. These and other needs are addressed by the inventions described herein.